基于改进YOLOv7的矿井人员检测算法

Mine Personnel Detection Algorithm Based on Improved YOLOv7

矿井人员的实时检测是建设智慧矿山必不可少的内容,通过视频监测井下人员,从而实现危险区域预警及联动控制,对于矿井安全生产具有重要意义。现阶段可见光图像识别技术针对井下昏暗环境中人员的辨识还有待完善。针对井下光照不均、煤尘干扰严重导致监控视频存在噪声多、图像模糊等问题,提出一种改进YOLOv7的矿井人员检测算法。首先,针对ELAN模块直接拼接形成通道隔离的问题,提出基于通道重组与特征关注的复杂场景检测方式;其次,针对特征融合结果未侧重预期目标且模型缺乏针对性策略提升小目标检测性能,在颈部多尺度融合网络添加ACmix模块,兼顾全局特征和局部特征,提升了算法对小目标的检测能力;最后,引入Efficient IOU Loss提升算法收敛速度的同时减小目标框及先验框高度和宽度的差值,实现更加精准的定位。通过公开行人数据集及自建矿井人员检测数据集验证表明:该算法较YOLOv7模型相比,检测精度提升了3.1%,达到89.4%;召回率提升了3.8%,达到86.4%;速度提升了15.8%,达到68.8FPS;满足矿井人员实时检测的工作要求。

Real-time detection of mine personnel is an essential part of the construction of intelligent mine.It is of great significance for mine safety production to realize early warning and linkage control of dangerous areas through video monitoring of underground personnel.At present,the visible light image recognition technology needs to be improved for the identification of personnel in the dim environment of underground coal mine.Aiming at the problems of more noise and image blur in the monitoring video caused by uneven illumination and serious coal dust interference in the underground,this paper proposes an improved YOLOv7 mine personnel detection algorithm.Firstly,aiming at the problem of channel isolation caused by direct splicing of ELAN modules,a complex scene detection method based on channel reorganization and feature attention is proposed.Secondly,since the feature fusion results does not focus on the expected target and the model lacks targeted strategies to improve the detection performance of small targets,an ACmix module is added to the neck multi-scale fusion network to take into account both global and local features,which improves the detection ability of the algorithm for small targets.Finally,efficient Intersection over Union(IOU)loss is introduced to improve the convergence speed of the algorithm and reduce the difference between the height and width of the target frame and the prior frame to achieve more accurate positioning.Through the verification of public pedestrian data sets and self-built mine personnel detection data sets,it is shown that the detection accuracy of the proposed algorithm is 3.1%higher than that of the YOLOv7 model,reaching 89.4%;the recall rate is increased by 3.8%to 86.4%,and the speed is increased by 15.8%to 68.8 FPS,meeting the mine personnel real-time detection work requirements.